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PMID: 16551372 Published · epublish English Journal Article Research Support, N.I.H., Extramural

SNPs3D: candidate gene and SNP selection for association studies.

BMC bioinformatics ·Vol. 7 ·2006-03-22 ·Pages 166

Yue P, Melamud E, Moult J

Abstract

The relationship between disease susceptibility and genetic variation is complex, and many different types of data are relevant. We describe a web resource and database that provides and integrates as much information as possible on disease/gene relationships at the molecular level. The resource http://www.SNPs3D.org has three primary modules. One module identifies which genes are candidates for involvement in a specified disease. A second module provides information about the relationships between sets of candidate genes. The third module analyzes the likely impact of non-synonymous SNPs on protein function. Disease/candidate gene relationships and gene-gene relationships are derived from the literature using simple but effective text profiling. SNP/protein function relationships are derived by two methods, one using principles of protein structure and stability, the other based on sequence conservation. Entries for each gene include a number of links to other data, such as expression profiles, pathway context, mouse knockout information and papers. Gene-gene interactions are presented in an interactive graphical interface, providing rapid access to the underlying information, as well as convenient navigation through the network. Use of the resource is illustrated with aspects of the inflammatory response and hypertension. The combination of SNP impact analysis, a knowledge based network of gene relationships and candidate genes, and access to a wide range of data and literature allow a user to quickly assimilate available information, and so develop models of gene-pathway-disease interaction.

MeSH Terms
Databases, Genetic Genetic Markers/genetics Genetic Predisposition to Disease/genetics Genetic Testing/methods Humans Internet Online Systems Polymorphism, Single Nucleotide/genetics User-Computer Interface
Chemicals
Genetic Markers
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Yue Peng
Center for Advanced Research in Biotechnology, University of Maryland Biotechnology Institute, Rockville, MD 20850, USA. [email protected]
Melamud Eugene
Moult John
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Article Info
Journal
BMC bioinformatics
Abbr.
BMC Bioinformatics
ISSN
1471-2105
Published
2006-03-22
Epub
2006-00-22
Pages
166
Language
English
Region
England
NLM ID
100965194
PMCID
PMC1435944
Subset
IM
Grants
NLM NIH HHS · R01 LM007174 · United States
NLM NIH HHS · R01 LM07174 · United States
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